Weight aggregation method based on principle of minimum cross-entropy in multiple attribute group decision-making

大一 何;Xiao Ling Chen;Jia Qiang Xu

School of Humanities and Economic Management

发表时间:2017-2-1

期 刊:Kongzhi yu Juece/Control and Decision

语 言:English

U R L: http://www.scopus.com/inward/record.url?scp=85019916584&partnerID=8YFLogxK

摘要

Based on the decision-makers' utility function, the attributes value matrix is converted to the subjective attributes value matrix to reflect their subjective judgments on the attributes value. By utilizing the entropy weighting technique, the decision-maker's subjective weight and objective weight of attributes are determined individually based on the subjective attributes value matrix and attributes value matrix. Then, based on the principle of minimum cross-entropy, all decision-makers' subjective weights are integrated into a single weight vector. Then, by applying the principle of minimum cross-entropy again, a weight aggregation method is proposed to combine the subjective and objective weight of attributes. Finally, a multiple attribute group decision-making(MAGDM) example of project choosing is presented to illustrate the procedure of the proposed method. The method proposed fully takes all decision-makers'various evaluation on a multiple attribute decision-making(MADM) problem into consideration, which provides a feasible method based on the information-theoretic entropy to aggregate weights in MAGDM problems.

关键词

Entropy weighting technique
MAGDM
Principle of minimum cross-entropy
Weight aggregation

相关科学

计算机科学
人工智能
软件
工程
自动化控制及系统工程
数学
控制与优化

文献指纹

数学

Cross-entropy

Group Decision Making

Aggregation

Attribute

Entropy

Multiple Attribute Decision Making

Utility Function

Judgment

Weighting

Evaluation

工程与材料科学

Agglomeration

Entropy

Decision making

期刊度量

Scopus度量

年份 CiteScore SJR SNIP
1996
1997
1998
1999 0.1
2000 0.101
2001 0.102
2002 0.166 0.1
2003 0.12 0.1
2004 0.133 0.355
2005 0.163 0.486
2006 0.181 0.36
2007 0.228 0.478
2008 0.261 0.586
2009 0.239 0.596
2010 0.253 0.571
2011 0.9 0.219 0.588
2012 0.9 0.266 0.733
2013 1 0.287 0.908
2014 0.9 0.26 0.859
2015 1 0.25 0.603
2016 1.1 0.249 0.548
2017 1.2 0.231 0.534
2018 1.3 0.224 0.577
2019 1.4 0.233 0.598
2020 1.4

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